Ranking Karnataka Districts by the Multidimensional Poverty Index (MPI) and by Applying Simple Elements of Partial Order Theory

This study focuses on the Multidimensional Poverty Index (MPI) ranking. The standard ranking process of the MPI produces a single total (linear) rank of units by simply ordering them from the best to the worst (or the inverse) as a function of their MPI score. However, units are not necessarily comparable regarding all 10 indicators simultaneously on which the MPI is based. We use the 2012/13 India District Level Household Survey wave four with a special focus on the State of Karnataka. By using partial order theory (i.e. the Hasse Diagram technique and the software package PyHasse), we found that, in Karnataka, the number of incomparabilities greatly exceeds the number of comparabilities. This indicates that the aggregation process leading to the MPI hides the individual role of indicators. We utilized a number of tools in partial order theory to analyze the comparabilities and incomparabilities. This included local partial order, antichain, and average height analysis. In contrast with the standard MPI ranking, partial order theory provides average height which does not only account for comparable districts, but also considers to what extent incomparable districts influence the position of a district in the ranking. We found that the results of partial order ranking deviate considerably from those of the MPI ranking. Given the extent of incomparabilities, for most of our sample, the MPI ranking does not provide an adequate ranking. The Hasse Diagram technique, can therefore be seen as a synthetic ranking tool or a robustness tool that complements the standard ranking process of the MPI.